Companies often generate large amounts of data in the normal course of operating their respective businesses. This data may then be evaluated by the companies in order to evaluate their business operations. The examination of the data may be for various specific purposes, such as determining how many sales have been made, how much money has been generated through these sales, how well particular business units are operating, and so on.
There may be many different aspects of a business that need to be examined in order to ensure that the business is being properly managed. Each of these aspects may be associated with various different types of data. It may therefore be necessary to examine each of the different types of data associated with a particular aspect of the business in order to properly analyze it. For example, in order to determine whether a particular marketing initiative is having a desired effect, it may be necessary to examine relevant contact information, purchase orders, revenues, and so on.
Often, the information that is associated with a particular aspect of a business is collected specifically for the purpose of analyzing that aspect of the business. The collection process may be manually initiated, and it may be necessary to review the information to ensure that only information which is relevant to the desired analysis is included. The information may then be manually analyzed. This process may, however, be inefficient for a number of reasons.
One inefficiency may arise from the fact that the information upon which a particular analysis is based may need to be collected specifically for the purpose of the analysis. There may be no existing information upon which the analysis may be based, or the existing information may be incomplete. For example, revenues information may be available, but there may be no information on the specific sources of the revenue, so it may be impossible to analyze the strength of specific regional markets (based on the revenue generated in the respective markets).
Another inefficiency may relate to the fact that it may not be apparent in the collection of information whether or not sufficient information has been collected. If the collection of information is initiated and terminated manually, there may be no way to know exactly what (or how much) information has been collected until after the collection has ended and the information can be examined. Thus, in one instance, if the collection of information ends too soon, the information may be insufficient and it may be necessary to entirely repeat the process of collecting the information. Even if the process of collecting the information can be continued from the point at which it was previously ended, time is lost in the intervening period, and it may not be possible to prepare the resulting analysis in a timely manner.
Another inefficiency may arise from the fact that the information upon which the analysis is based may itself have to be examined before it can be determined that the data is appropriate for the analysis. For example, if the desired analysis is to determine the strength of specific regional markets, it may be necessary to examine the collected information to ensure that only information for the relevant markets is used in the analysis. Information that is not relevant to the desired regional markets may need to be discarded before the information is analyzed.
Still another inefficiency may arise from the fact that various types of analyses may be based upon overlapping sets of information. In other words, a portion of the information upon which a first analysis is based may be common to the information upon which a second analysis is based. In collecting information for the first analysis and then collecting information for the second analysis, the collection of the common information may be repeated.